Collision avoidance in human-robot interaction using kinect vision system combined with robot’s model and data

Hugo Nascimento (1, 2) , Martin Mujica (2) , Mourad Benoussaad (2)

1 Federal University of Pernambuco [Recife]
2 LGP - Laboratoire Génie de Production

Human-Robot Interaction (HRI) is a largely ad-dressed subject today. Collision avoidance is one of main strategies that allow space sharing and interaction without contact between human and robot. It is thus usual to use a 3D depth camera sensor which may involves issues related to occluded robot in camera view. While several works overcame this issue by applying infinite depth principle or increasing the number of cameras, we developed in the current work a new and an original approach based on the combination of a 3D depth sensor (Microsoft® Kinect V2) and the proprioceptive robot position sensors. This method uses a principle of limited safety contour around the obstacle to dynamically estimate the robot-obstacle distance, and then generate the repulsive force that controls the robot. For validation, our approach is applied in real time to avoid collision between dynamical obstacles (humans or objects) and the end-effector of a real 7-dof Kuka LBR iiwa collaborative robot.Several strategies based on distancing and its combination with dodging were tested. Results have shown a reactive and efficient collision avoidance, by ensuring a minimum obstacle-robot distance (of ≈ 240mm), even when the robot is in an occluded zone in the Kinect camera view.

Mots clés: Human-robot interaction; Space sharing; 3D depth camera sensor; Occluded robot; Proprioceptive robot position sensors; Robot-obstacle distance; Collaborative robot; Reactive collision avoidance; Kinect camera view; Kinect vision system; Kuka LBR iiwa collaborative robot; End-effector

Domaines: Robotique [cs.RO]

Hugo Nascimento, Martin Mujica, Mourad Benoussaad. Collision avoidance in human-robot interaction using kinect vision system combined with robot’s model and data. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 2020, Las Vegas, United States. pp.10293-10298, ⟨10.1109/IROS45743.2020.9341248⟩. ⟨hal-03223180⟩